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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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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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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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An Immutable Framework for Smart Healthcare Using Blockchain Technology

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computer Systems Science & engineering 2023年第7期46卷 165-179页

作者： Faneela Muazzam A.Khan Suliman A.Alsuhibany Walid El-Shafai Mujeeb Ur Rehman Jawad Ahmad Department of Computer Science Quaid-i-Azam UniversityIslamabad45320Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Science Security Engineering LaboratoryPrince Sultan UniversityRiyadh11586Saudi Arabia James Watt School of Engineering University of GlasgowG128QQUK School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare *** Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical *** traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare *** article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated *** proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy *** efficacy of the proposed framework is evaluated in the context of service execution time and *** experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.

关键词： Blockchain technology healthcare applications cybersecurity services IoMT DoS EHR

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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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Empowering Software engineering Students: The Role of Gamification in Promoting Learning Independence 7

Empowering Software Engineering Students: The Role of Gamifi...

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7th International Conference on Vocational Education and Electrical engineering, ICVEE 2024

作者： Hidayat, Wahyu Nur Hamdan, Achmad Ichwanto, Muhammad Aris Muslihati Cakir, Gulsun Kurubacak Sari, Rahajeng Kartika Afni, Nur Hisyam, Mochammad Raffi Fadhil Universitas Negeri Malang Faculty of Engineering Department of Electrical Engineering and Informatics Malang Indonesia Universitas Negeri Malang Faculty of Vocational Department of Electronic Systems Engineering Technology Malang Indonesia Universitas Negeri Malang Faculty of Engineering Department of Civil Engineering and Planning Malang Indonesia Universitas Negeri Malang Faculty of Education Science Department of Guidance and Counseling Malang Indonesia Ankara Haci Bayram Veli University College of Communication Department of Journalism Ankara Turkey Smk Negeri 10 Malang Department of Computer Network and Telecommunication Engineering Malang Indonesia

ISBN: (纸本)9798331505103

The role of learning media greatly supports the learning process, increasing motivation and interest in the process. Learning media can also be a place to improve competence optimally through bootcamp training menus and the like with more specific training objectives. The main objective of this study is to develop a gamification-based website to enhance students' preparedness for their Final Competency Test by tackling challenges in comprehending programming absent practical experience. The learning method implemented in this website is case-based learning that integrates dynamic coding activities. The research method uses the Plomp approach with 3 main stages, namely preliminary research, prototyping, and assessment stage. The trial of the gamification website began with media and content validation followed by small group testing and large group testing. Validation by media and material experts led to the execution of small group trials (95.7%) and large group trials (91.7%), producing exceptionally positive outcomes. The results of the small group test with 12 respondents produced a very suitable value with a score of 95.7 and the large group test with 27 respondents produced a score of 9 1. 7 % with very suitable criteria. This indicates that the web-based platform is highly viable for assisting vocational students in acquiring software development skills, enhancing their preparedness for practical IT positions such as software developers and system analysts. Subsequent study involved the incorporation of gamification, such as computer-supported collaborative learning. © 2024 IEEE.

关键词： Software testing

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Research and Analysis of Different Real Cases, with use AAI 16

Research and Analysis of Different Real Cases, with use AAI

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16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023

作者： Kostadinova, I. Dimitrov, G. Martsenyuk, V. Rancic, D. Dirgova-Luptakova, I. Jovancevic, I. Trenchev, I. Toleva-Stoimenova, S. Petrov, P. Faculty of Information Science Sofia Bulgaria Department of Computer Science and Automatics Poland Faculty of Electronic Engineering Serbia Faculty of Natural Sciences Slovakia Montenegro University of Economics-Varna Bulgaria

ISBN: (纸本)9798350347029

This article is fulfilled within the framework of Erasmus+ project "The Future is in Applied Artificial Intelligence"(FAAI) and examines the study of practical solutions implemented using applied artificial intelligence. The research was done by preparing an online survey containing a total of 7 questions, open and closed. The purpose of the study is to find real working applications of applied artificial intelligence projects, describe their application in what field, and record the name of the projects found to describe their activity. The study was done by looking at cases all over the world. The analysis of the data provides insight in several directions:-in which countries are more real cases of artificial intelligence solutions used-what is the distribution of realized cases-depending on whether the country is a member of the EU or not EU.-In what category is the real case developed.-whether the country of the real case works in collaboration with other countries or implements the real case only the *** research and analysis done provide a clear picture of the developed projects using artificial intelligence. The obtained results will guide in what areas to organize the practical training. Also, the research would help future AI application developers. © 2023 IEEE.

关键词： Applied Artificial Intelligence Artificial Intelligence FAAI Good Practices Training

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