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Novel Framework of Segmentation 3D MRI of Brain Tumors

computers, Materials & Continua 2023年第2期74卷 3489-3502页

作者： Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Embedded Network Sys.and Tec. Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free *** proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the *** framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle *** Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)*** framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,*** experiments that have been conducted have proved the efficiency of the proposed framework a

关键词： MRI Rician noise speckle noise segmentation deep learning

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Maintaining Text Legibility Regarding Font Size Based on User Distance in Mobile Devices: Application Development and User Evaluation

Maintaining Text Legibility Regarding Font Size Based on Use...

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2023 IEEE International Symposium on Multimedia, ISM 2023

作者： Mallas, Andreas Papadatou, Hara Xenos, Michalis University of Patras Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350395761

The text's legibility can dramatically influence any device's usability, and a wealth of research has examined the ideal font characteristics for various displays regarding legibility and readability. However, mobile devices have an additional factor that influences viewing conditions;their greatest strength, i.e., portability, is their biggest weakness in this regard. Furthermore, the freedom to use the device in diverse situations and positions (sitting, standing, in transit, or while waiting in line) can influence various facets related to legibility, such as the user's holding position, operating style, and the environment's light directed to the phone. The application developed aims to maintain user-preferred text font size -in all typical distances that users operate their phones- by automatically adjusting the font size according to the distance between the user and the device by using the device's camera to calculate the distance in real-time. A two-part experiment (n = 34) revealed that the application scored an excellent usability score while the visual landmarks used showed a promising start at addressing an issue related to our approach. © 2023 IEEE.

关键词： Display devices

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Influence Maximization in Dynamic Social Networks Under Partially Observable Environments

Influence Maximization in Dynamic Social Networks Under Part...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Smani, Gkolfo I. Megalooikonomou, Vasileios University of Patras Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350327595

Influence and influence diffusion have been studied widely in social networks. Influence maximization is the problem of detecting a set of influential nodes in a social network, which represents relationships among individuals. Although, most of the existing works on this task focus on static networks, in this paper we study the problem of influence maximization in dynamic social networks where the changes that occur over time can be observed by periodically probing nodes to update their connections. The goal of this work is to probe a subset of nodes in a social network so that the actual influence diffusion process in the network can be best uncovered with the probing nodes. We propose three algorithms, MaxC, MaxDC and MaxT, to approximate the optimal solution with probing nodes, which achieve improvement on estimating the number of activated nodes over state-of-the-art-method by 2.88%, 4.95% and 5.39%, respectively. © 2023 IEEE.

关键词： core number diffusion dynamic social networks graphs maximization influence probing social influence trussness

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Eye-Tracking Based Automatic Summarization of Lecture Slides 5

Eye-Tracking Based Automatic Summarization of Lecture Slides

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5th International Conference on computer and Applications, ICCA 2023

作者： Vazaios, Stylianos Mallas, Andreas Xenos, Michalis University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350303254

This paper presents a novel approach to automatically summarize lecture slides using eye-tracking data. The tool generates personalized summaries by analyzing learners' visual attention patterns, with the aim of reducing information overload and enhancing comprehension. While existing text summarization methods often overlook individual preferences and attention patterns, our proposed approach considers all slide elements, including text and visuals. It dynamically selects areas of interest based on learners' fixation points. The tool is developed as a web application, enabling users to access presentations, receive personalized summaries, and save them for optimized studying. It incorporates features such as administration and settings, slides uploading, and summary viewing. To evaluate the tool, we conducted an experiment involving 62 participants to assess usability and satisfaction with the automated summaries. The evaluation results indicated positive feedback in terms of ease of use and visual appeal. Participants expressed satisfaction with the generated summaries and were given the option to select their preferred summarization percentage. Our analysis revealed that summarization preferences are not related to prior knowledge. Overall, this tool demonstrates the potential of personalized summarization based on eye-tracking data, thereby enhancing learning experiences. © 2023 IEEE.

关键词： Eye tracking

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Sentiment Analysis with the Use of Transformers and BERT 14

Sentiment Analysis with the Use of Transformers and BERT

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Douka, Elisavet Perikos, Isidoros Hatzilygeroudis, Ioannis University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350318067

Recognizing the emotional content of natural language sentences can improve the way humans communicate with a computer system by enabling them to recognize and imitate emotional expressions. The field of emotion recognition occupies an important place in the applications of artificial intelligence. The rapid increase in the popularity of social media has created the need to study and document their use. In this paper, both classical classification algorithms and various neural network architectures were tested. In the context of this paper, we designed and developed deep learning methods and BERT-based implementations for recognizing emotional content in user-generated data. Extensive experiments were conducted using these models on a variety of textual data and all the designed methods were evaluated. © 2023 IEEE.

关键词： Deep learning

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HealthAir: A Crowdsourcing mHealth Platform for Air Pollution Effects on Citizens Health Status 19

HealthAir: A Crowdsourcing mHealth Platform for Air Pollutio...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Katsidimas, Ioannis Nikoletseas, Sotiris Panagiotou, Stefanos H. Spyropoulou, Christina University of Patras Computer Engineering and Informatics Department Greece Greece

ISBN: (纸本)9798350346497

Crowdsourcing systems offer numerous advantages for acquiring data, which can contribute to the development of modern concepts such as Smart Cities. Air quality in urban areas has been identified as a crucial theme and requirement, towards ideal city of the future that can be adapted upon its citizens' needs and comfort. Thus, besides the sensing infrastructure, a complementary system must be implemented in order to enable the public authority and municipality awareness over citizens' personalised and subjective input, in a cost efficient way that can be easily upscaled. In this work, we present HealthAir, an mHealth application alongside its subsystems (namely a web application and a smartwatch version), that aims to increase and complement the sensing capacity of a Smart City, with respect to the air quality-wise health of the citizens. This is achieved based on questionnaires and surveys approved by the health community, and services and features that implement a bidirectional interaction between the system and the users. Finally, users' data and air quality measurements from a third party platform are combined and presented for the case study realization and demonstration. © 2023 IEEE.

关键词： Air quality

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Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

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computers, Materials & Continua 2023年第11期77卷 1795-1814页

作者： Arsal Javaid Areeb Abbas Jehangir Arshad Mohammad Khalid Imam Rahmani Sohaib Tahir Chauhdary Mujtaba Hussain Jaffery Abdulbasid S.Banga Department of Electrical and Computer Engineering COMSATS University IslamabadLahore CampusLahore54000Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah211Oman

To detect the improper sitting posture of a person sitting on a chair,a posture detection system using machine learning classification has been proposed in this *** addressed problem correlates to the third Sustainable Development Goal(SDG),ensuring healthy lives and promoting well-being for all ages,as specified by the World Health Organization(WHO).An improper sitting position can be fatal if one sits for a long time in the wrong position,and it can be dangerous for ulcers and lower spine *** novel study includes a practical implementation of a cushion consisting of a grid of 3×3 force-sensitive resistors(FSR)embedded to read the pressure of the person sitting on ***,the Body Mass Index(BMI)has been included to increase the resilience of the system across individual physical variances and to identify the incorrect postures(backward,front,left,and right-leaning)based on the five machine learning algorithms:ensemble boosted trees,ensemble bagged trees,ensemble subspace K-Nearest Neighbors(KNN),ensemble subspace discriminant,and ensemble RUSBoosted *** proposed arrangement is novel as existing works have only provided simulations without practical implementation,whereas we have implemented the proposed design in *** results validate the proposed sensor placements,and the machine learning(ML)model reaches a maximum accuracy of 99.99%,which considerably outperforms the existing *** proposed concept is valuable as it makes it easier for people in workplaces or even at individual household levels to work for long periods without suffering from severe harmful effects from poor posture.

关键词： Posture detection FSR sensor machine learning real-time KNN

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Introduction to Heterogeneous Robotic Systems in 5G and Beyond Networks

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IEEE WIRELESS COMMUNICATIONS 2023年第2期30卷 64-65页

作者： Lu, Huimin Ho, Pin-Han Khalfi, Bassem Verikoukis, Christos Kyushu Institute of Technology Japan Department of Electrical and Computer Engineering The University of Waterloo Canada Oregon State University CorvallisOR United States University of Patras Department of Computer Engineering & Informatics Greece

Remote intelligence in the application of robotics and the autonomous system relies heavily on seamless wireless connections. The 5G mobile network technology meets traditional manufacturing enterprises' application requirements for wireless networks based on robot transformation and upgrading, robot interconnection, and remote interactive applications in production. However, there exists many challenging 5G communication issues, such as different communication protocols in the system varies with different robots and no dis-ruptive changes to the physical layer.

关键词： Special issues and sections Robots 5G mobile communication Physical layer Autonomous systems Heterogeneous networks

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High-Performance and Low-Cost Approximation of ANN Sigmoid Activation Functions on FPGAs 12

High-Performance and Low-Cost Approximation of ANN Sigmoid A...

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Tatas, Kostantinos Gemenaris, Michalis Frederick University Department of Electrical Engineering Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350321074

Neural network implementations on FPGAs have received significant attention from the research community due to their superior performance in tasks such as computer vision and the need to perform them on edge devices. Most efforts have focused on architecture optimizations such as pruning and operations such as the multiplications and additions with less attention on the ANN activation function. In this paper a hardware implementation of an artificial neural network on Field Programmable Gate Arrays (FPGA) is presented. A digital system architecture is designed with the purpose of reducing the delay of implementation and minimizing the LUTs in the FPGA. The designed architecture is described using Very High-Speed Integrated Circuits Hardware Description Language (VHDL). The proposed method utilizes a known piecewise linear approximation of the sigmoid function, and additionally exploits properties of fixed-point binary number representation to efficiently reduce delay and the amount of LUTS required Compared with previously published implementations of the same function and FPGA technology, the proposed method is shown to achieve a reduction between 33% and 95% in LUTs with no reduction in accuracy. © 2023 IEEE.

关键词： Neural networks

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Developing an Anti -Phishing Large Language Model: A Focus Group Study on Human, Technological, and Legal Factors 15

Developing an Anti -Phishing Large Language Model: A Focus G...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Thomopoulos, George A. Kiortsi, Panagiota Sigalas, Damianos Dumi Kosmopoulos, Dimitris Igglezakis, Ioannis Fidas, Christos University of Patras Department of Electrical and Computer Engineering Greece University of Thessaloniki Faculty of Law Aristotle Greece University of Patras Department of Computer Engineering and Informatics Greece

ISBN: (纸本)9798350368833

The phishing problem poses a significant threat in modern information systems, putting both individuals and businesses at risk of financial and professional harm. Owing to social media's rapid development and widespread appeal, deception of this sort is hurting millions of people and growing more dangerous. The current work, as part of the AILA (Artificial Intelligence-driven Framework and Legal Advice Tools for Phishing Prevention and Mitigation in Information Systems) project, aims to specify and validate an AI-driven multifactor (human, technology and legal) anti-phishing data model, with the implementation of focus group studies. The findings assist to provide human, technology, and legislative user model endpoints that will be identified and discussed for explicit and implicit user modeling, which will guide the development of the corresponding AI-driven user modeling and profiling mechanisms. To this end a Large Language System is planned to be employed. © 2024 IEEE.

关键词： Phishing

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