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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Purnama, Iwan Pane, Rahmadani Sooai, Adri Gabriel Edi, Firman Chamid, Ahmad Abdul Adi, Puput Dani Prasetyo Universitas Labuhanbatu Faculty of Science and Technology Department of Information Technology Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Batam City Indonesia Muria Kudus University Department of Informatics Engineering Kudus Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This article presents a simulation study on the use of LoRaWAN technology in autonomous vehicles. The research focuses on developing an Auto-Handover Gateway for Autonomous Vehicles, utilizing LoRaWAN parameters such as bit rates, spreading factor (SF), packet loss, and received signal strength. These parameters are tested in simulations of autonomous vehicle movements, which include both regular and random motion patterns. The aim is to assess the performance of LoRaWAN in different scenarios and its impact on communication in autonomous vehicle systems. The simulation results show several key trends. As the vehicle moves further, the spreading factor (SF) increases, and bit rates tend to decrease, indicating a drop in communication performance over longer distances. The research also identifies that the gateway with the closest proximity to the vehicle is selected, both in random and non-random movement scenarios. Additionally, packet loss increases as the distance between the vehicle and the gateway grows, highlighting the challenges of maintaining reliable communication over long distances. These findings provide insights into optimizing LoRaWAN parameters for real-time communication in autonomous vehicles, contributing to the development of intelligent vehicle networks. © 2024 IEEE.

关键词： Packet loss

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Numerical Modelling of The System of Doubly Nonlinear Diffusion Equations with Convective Transfer and Damping

Numerical Modelling of The System of Doubly Nonlinear Diffus...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Sadullaeva, Shakhlo Azimbaevna Anarova, Shahzoda Amanbayevna Khaydarov, Abdugaffor Fayzullayeva, Zarnigor Nazirova, Dilorom Mirkhodzhaeva, Nazhiba departament Intellegent systems Joint Belarusian-Uzbek Intersectoral Institute of Applied Technical Qualifications Tashkent Uzbekistan Department of Convergence of Digital Technologies Computer Engineering Faculty Tashkent University of Information Technologies Tashkent Uzbekistan Department of Applied Mathematics and Computer Analysis National University of Uzbekistan Tashkent Uzbekistan Department of Informatics Computer Engineering Faculty Tashkent University of Information Technologies Tashkent Uzbekistan Department of Applied Mathematics and Computer Analysis NUUz Tashkent Uzbekistan

ISBN: (纸本)9798400716928

Currently, studies of double nonlinear equations and systems of double nonlinear equations representing many processes found in nature are considered relevant and necessary. These equations and systems, as a rule, are nonlinear, degenerate, and have properties that are absent in linear theory and therefore make them quite difficult for both analytical and numerical analysis of the solution. For this reason, great attention is paid to the research of nonlinear mathematical models, the construction of efficient numerical solution schemes and algorithms, and the creation of software using the capabilities of computer technologies. In this article, the mathematical model of the diffusion process in a two-component nonlinear medium under the influence of time-dependent convective transfer and damping is studied. The calculation scheme, algorithm, and software for nonlinear problems were developed. Based on a study of the qualitative properties of a nonlinear mathematical model of a reaction-diffusion system, computer modeling of reaction-diffusion processes was carried out. The developed computational schemes, algorithms, and software packages for solving systems of parabolic equations with double nonlinearity provide high productivity in studying the theory and processes of numerical solutions of such problems. © 2024 ACM.

关键词： Nonlinear equations

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Optimized Price Prediction of Cryptocurrencies using Deep Learning on High-Volume Time Series Data

Optimized Price Prediction of Cryptocurrencies using Deep Le...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Gerasimos Vonitsanos Andreas Kanavos Foteini Grivokostopoulou Spyros Sioutas Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Computer Technology Institute and Press “Diophantus“ Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Statistical models, enhanced by deep learning techniques, have become pivotal in various predictive tasks, including financial forecasting. This paper addresses the challenge of predicting cryptocurrency prices, utilizing a dataset comprising various cryptocurrencies treated as time series. We employ several deep neural network architectures-including Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Bidirectional LSTM networks-to forecast future prices. Our methodology involves a detailed analysis of cryptocurrency time series to inform the design of these networks. The performance of each model is rigorously compared, highlighting their predictive capabilities in the context of cryptocurrency markets. This study not only contributes to the empirical literature by applying advanced neural networks to high-volume financial data but also provides a comparative analysis that may guide future applications of deep learning in economic forecasting.

关键词： Deep learning Biological system modeling Time series analysis Predictive models Multilayer perceptrons Economic forecasting Big Data applications Cryptocurrency Context modeling

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An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

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computer Systems Science & engineering 2023年第7期46卷 701-717页

作者： Ahmed Ben Atitallah Mohamed Amin Ben Atitallah Yahia Said Mohammed Albekairi Anis Boudabous Turki MAlanazi Khaled Kaaniche Mohamed Atri Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia Laboratory of Informatics Gaspard-MongeA3SIESIEE ParisCNRSGustave Eiffel UniversityFrance LETI ENISUniversity of SfaxSfaxTunisia Remote Sensing Unit College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) University of MonastirTunisia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia

The challenge faced by the visually impaired persons in their day-today lives is to interpret text from *** this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into *** system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been *** this study,an acceleration of the GCM algorithm(AGCM)is *** AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original *** assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech *** text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.

关键词： Text recognition system GCM AGCM OCR color images graphical interface

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Ultrasound Image Analysis in Breast Cancer: A Comparative Study of Decision Trees and Random Forests 16

Ultrasound Image Analysis in Breast Cancer: A Comparative St...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Sivagami, S. Sathya, A. Afsin, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Bio Informatics Tamil Nadu Chennai India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505264

Breast cancer remains a leading cause of cancer-related mortality among women globally, underscoring the critical need for early detection and accurate diagnosis. Ultrasound imaging serves as a non-invasive diagnostic tool for identifying breast abnormalities, yet the complexity of these images presents significant interpretation challenges. This study explores the application of Machine Learning (ML) models, specifically Decision Tree (DT) and Random Forest (RF) classifiers, for the classification of breast cancer ultrasound images into benign, malignant, and normal categories. Data augmentation techniques are employed to address class imbalance and enhance model generalization using the Breast Cancer Ultrasonic Image dataset. Among preprocessing steps, pixel values were normalized to provide a consistent input for the models. RF model performed better than DT with an accuracy of 88.75% against an accuracy of 86.25% and those masks for ultrasound images contribute a lot to the overall classification accuracy. In summary, the results of our study can provide support for grand challenges in health and emphasize implications such as the potentiality of ML models to advance breast cancer diagnosis and it implies a necessity for an even further expansion that will allow complete patient outcomes optimization. © 2024 IEEE.

关键词： Radio frequency Accuracy Ultrasonic imaging Training data Predictive models Breast cancer Data models Decision trees Random forests Tumors

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Generalization Evaluation of Seq2seq based Fog Computing Application Placement Algorithm on Limited Dataset 3

Generalization Evaluation of Seq2seq based Fog Computing App...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Pakpahan, Michael S. M. Nugroho, Lukito Edi Widyawan, Widyawan Wardhana, Ajie Kusuma Muhammad Ardian, R.A. Astagenta, Rangga Satria Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Dept. Informatics Yogyakarta Indonesia

ISBN: (纸本)9798350394559

Addressing its growing number and vital role, decentralization of cloud computing becoming a necessity. Fog computing aims to bring application closer to the data source-typically at the network's edge by leveraging local resources to provide faster data processing and decision-making. Fog computing then has to place application strategically to use its limited fog resource to improve application performance metrics. This problem known as Fog Application Placement Problem (FAPP) has been approached using previous methods that rely on rules and prior knowledge that may not be adaptive, but rather being overly specialized to specific problems. Deep learning with its learning mechanism, can offer more adaptable and dynamic solutions for a wide range of scenarios, especially in fog network that continuously evolve. This research investigate Seq2seq placement model inherent limitations, notably the impracticality of generating every possible pattern from all potential request configurations. We aim to address and answer the following critical questions: 1) How does the model's performance vary when confronted with unseen requests or an augmented number of modules, especially considering the limitations in training data?;2) Can the seq2seq model, even with its training limitations, adhere to the heuristic rules of the dataset when dealing with unfamiliar problems? This research shows that in similar availability, there is a reduction of almost half of the response time with 183.03 ms, 2.93 number of hops, and 0.87 megabyte of transmitted messages against the hop3 algorithm. Moreover, we highlight the ability of seq2seq model to follow heuristic rules in unseen scenarios. © 2023 IEEE.

关键词： Fog computing

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Simulating inexact DNNs for use in Custom Hardware Architectures

Simulating inexact DNNs for use in Custom Hardware Architect...

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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Dimitrios Danopoulos Georgios Zervakis Dimitrios Soudris School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Computer Engineering & Informatics University of Patras Patras Greece

In response to the growing complexity of Deep Neural Network (DNN) models, the paradigm of approximate computing has emerged as a compelling approach to strike a balance between computational efficiency and model accuracy. Approximate computing involves intentionally allowing errors using inexact computations, exploiting the inherent resilience of many applications to such inaccuracies. While current state-of-the-art approaches leverage approximate multipliers for approximate DNN accelerators, there has been a notable difficulty in emulating such DNNs on common frameworks such as PyTorch, as they do not inherently support inexact arithmetic. This paper introduces a seamless PyTorch plugin, to evaluate inexact DNN models while having GPU acceleration to enable rapid emulation. Additionally, we support approximate-aware retraining and perform evaluation on several popular convolutional neural networks on Cifar10 and ImageNet datasets.

关键词：

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Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

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computers, Materials & Continua 2023年第9期76卷 3029-3047页

作者： Muhammad Attique Khan Reham R.Mostafa Yu-Dong Zhang Jamel Baili Majed Alhaisoni Usman Tariq Junaid Ali Khan Ye Jin Kim Jaehyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Informatics University of LeicesterLeicesterUK Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61413Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and ***,it always requires an expert person for the ***,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the *** paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization ***-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI *** haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast ***,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling ***,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better *** the information was increased,computational time also *** issue is resolved using a hybrid feature optimization algorithm that chooses the best classification *** experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,*** proposedmethod is comparedwith several recent studies andoutperformed *** addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

关键词： Brain tumor haze contrast enhancement deep learning transfer learning features optimization

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A VR Intervention to Develop Social Skills in Children with ASD: An Expert Evaluation 24

A VR Intervention to Develop Social Skills in Children with ...

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Proceedings of the 2024 ACM International Conference on Interactive Media Experiences

作者： Yujing Zhang Conor Keighrey Niall Murray Computer & Software Engineering Technological University of the Shannon Ireland Department of Engineering and Informatics Technological University of the Shannon Ireland Electronics & Informatics Technological University of the Shannon Ireland

ISBN: (纸本)9798400705038

Social interactions hold immense significance in the lives of individuals. Children with a diagnosis of autism spectrum disorder (ASD) may face difficulties when engaging in social situations. These challenges often manifest as neurodivergent behaviours and a reduced willingness to engage in social sharing. Sharing is an integral aspect of social interaction. A lack of awareness or a limited inclination to share can hinder the acquisition of vital social skills. In this paper, a virtual reality (VR) application inspired by the Social Story™ intervention is presented. The aim of the work is to help children with ASD understand social scenarios and gain sharing skills. The application can be used in two configurations: 2D screen and head-mounted display (HMD). Since the system is being designed for a vulnerable group, as part of the iterative application design process, we solicited opinions from six expert professionals who work with children with ASD. They tested, evaluated (questionnaires and physiological data capture) and provided open feedback on the system. The key recommendation from the expert assessors is that the HMD-based interventions are more suitable as an advanced teaching tool for showing skills and knowledge, whereas screen-based interventions could be used for daily practice in children with ASD. Experts evaluated the two configurations from three factors: immersion, enjoyment, and comprehension. In terms of physiological responses, there was a greater increase in electrodermal activity (EDA) responses for the HMD-based intervention compared to the 2D intervention. The significant potential of such a system was highlighted by the expert users.

关键词： VR intervention autism expert evaluation sharing social skills

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A Network Simulator as a Service for Demanding Performance Evaluation of Wireless and IoT Scenarios 2

A Network Simulator as a Service for Demanding Performance E...

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2nd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022

作者： Antonopoulos, Konstantinos Ringas, Dimitrios Christopoulou, Eleni Keramidas, Georgios Antonopoulos, Christos Voros, Nikolaos University of Peloponesse Electrical and Computer Engineering Department Patras Greece Ionian University Department of Informatics Corfu Greece School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9781665498258

ISBN: (纸本)9781665498258

This paper presents the design, development and evaluation of a novel Network Simulator as a Service infrastructure. It is based on a prominent open-source discrete-event network simulator which is extended to offer advanced batch simulation execution, results acquisition, post-processing analysis and presentation features, as well as machine learning optimisations to reduce the overall processing load. In that respect, we have developed a software system, which allows researchers to define simulation scenarios along with a set of parameters and to commission simulation executions in large scale thus delivering to the end user a service that is novel, robust, extendible and extensible. It has been thoroughly evaluated within ESDA Lab (UoP), proving its feasibility and demonstrating its ability to leverage machine learning techniques in order to automatically identify and exclude simulations that are classified as ones that will not meet defined performance requirements, thus drastically reducing simulation workload, delay and resource utilisation. © 2022 IEEE.

关键词： Wireless networks

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