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FUSION-BASED ALGORITHM FOR X-RAY PHASE CONTRAST IMAGING TECHNIQUES

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第3期86卷 157-176页

作者： Rasinar, Cezara Safca, Nicoleta Cotorceanu, Elena Ciobanu, Ionut Stefanoiu, Dan Faculty of Automatic Control and Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Magurele Romania National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Chemical Engineering and Biotechnologies National University of Science and Technology POLITEHNICA Bucharest Romania

Phase Contrast X-ray Imaging represents a technique that has shown remarkable potential in the research field, by providing better visualization of soft tissue, high-contrast images, and high spatial resolution. In the phase contrast imaging technique, the extraction of the three images represented by attenuation, differential phase contrast, and dark-field contrast is essential, providing, in a specific way, information about the nature of the analyzed tissue. This paper introduces a new method based on the three acquired images merging while adjusting the weighting factors, aiming to identify the interest object and to extract the information provided by each contrast channel. In this regard, a fusion-based algorithm was applied to images acquired, using a 5.66 m long Interferometer Talbot-Lau with ultrahigh sensitivity of 0.84 µradians and mean energy of 30 keV. The obtained results are quite promising, as shown in the end of the article. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Interferometers

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PuzMaty: Supporting Puzzle Mats Design Creation

PuzMaty: Supporting Puzzle Mats Design Creation

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IEEE/SICE International Symposium on System Integration

作者： Sarii Yamamoto Jiajun Wang Liwei Chan Yuta Sugiura Faculty of Information and Computer Science Keio University Japan Department of Computer Science and Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Puzzle mats made of cushioned material are widely used in environments like homes and playrooms to prevent injuries from infants and toddlers falling. The mats feature puzzle-like edges, allowing users to freely adjust their size and shape to fit the space where the mat is placed. This study proposes "PuzMaty," a design interface for drawing patterns of animals, numbers, letters, etc. using puzzle mats of different colors. This interface is expected to make the puzzle mat function not only as a safety measure but also as an interior decoration. It is also likely to reduce the burden of childcare when placing mats because it makes the number of mats required for a particular space more intuitive.

关键词： Pediatrics Shape Animals Color System integration Safety Colored noise Injuries Creativity

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An IoT Machine Learning Model-Based Real-Time Diagnostic and Monitoring System 4th

An IoT Machine Learning Model-Based Real-Time Diagnostic and...

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Awotunde, Joseph Bamidele Misra, Sanjay Ajagbe, Sunday Adeola Ayo, Femi Emmanuel Gurjar, Ramchandra Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Science and Communication Ostfold University College Halden Norway Department of Computer Engineering Ladoke Akintola University of Technology Ogbomosho Nigeria Department of Mathematical Sciences Olabisi Onabanjo University Ago-Iwoye Ogun State Nigeria Shri Govindram Seksaria Institute of Technology and Science Indore India

ISBN: (纸本)9789819900848

Internet of things (IoT) and machine learning (ML) have recently changed the paradigm in the area of applications in health-care systems with significant results. The ML-based models enabled with IoT-based systems have greatly expedited the medical diagnosis, real-time monitoring, and treatment in the health-care system, thus reducing the burden of health-care processes and the cost of taking care of patients by caregivers and physicians. Therefore, in this paper, a framework is proposed based on an ML model-enabled IoT-based system for real-time diagnosis of various diseases using data collected from wearable devices and sensors attached within the body of patients. The captured data are stored using an integrated cloud database for further preprocessing and processes using ML-based models. ML model was used to select the most relevant features from the cloud database and thus use the preprocessed data for the diagnosis of various diseases. The findings from the study show that the XGBoost performs reliably better when compared with other existing ML-based models with an accuracy of 95.7%, recall of 95.9%, precision of 96.0%, F1-score of 94.8%, and Receiver Operating Characteristic Curve (ROC) of 94.3%. The results proposed a framework to show that the system can be widely used to diagnose various diseases and can be used in remote areas for better monitoring of patients. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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A User Interactive Shift Scheduling and Timetabling System Based on Evolutionary Computation for Private Tutoring Schools 29th

A User Interactive Shift Scheduling and Timetabling System ...

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29th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2024

作者： Suzuki, Yosuke Mizuno, Kazunori Graduate School of Engineering Takushoku University Tokyo Japan Department of Computer Science Faculty of Engineering Takushoku University Tokyo Japan

ISBN: (纸本)9789819645954

A shift schedule is absolutely necessary to manage the work of each employee. However, manually constructing the schedule taken account of employee preference can impose a large burden on a constructor. In private tutoring school, a shift schedule should make arrangement of the requests of students in addition to teachers’ preference, being necessary to construct simultaneously a shift schedule for each teacher and a timetable for each student. In this paper, we have proposed a two-phase optimization method to find such schedules: shift scheduling using memetic algorithms and timetabling using simulated annealing. We have demonstrated that the proposed method, in particular memetic algorithms in the first phase, can be more effective for constructing a shift schedule and timetable that are taken account of both requests of teachers and students than the previous method using simple genetic algorithms and simulated annealing. In addition, we have developed an interface that allows users to interactively check and make minor modifications to the automatically created shift schedule. This user interface can be useful for the user to instantly check where constraints have been violated and assist in making corrections by the user. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Simulated annealing

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Learning Features Using an optimized Artificial Neural Network for Breast Cancer Diagnosis

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SN computer science 2022年第3期3卷 229页

作者： AlShourbaji, Ibrahim Kachare, Pramod Zogaan, Waleed Muhammad, L.J. Abualigah, Laith Department of Computer and Network Engineering Jazan University Jazan 45142 Saudi Arabia Department of Electronics and Telecommunication Engineering Ramrao Adik Institute of Technology Nerul Maharashtra Navi Mumbai 400706 India Department of Computer Science Jazan University Jazan 45142 Saudi Arabia Department of Computer Science Faculty of Science Federal University of Kashere P.M.B. 0182 Gombe Nigeria Faculty of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan

Breast cancer (BC) has been one of the significant causes of death worldwide, and its early detection can play a vital role in increasing the survival rate of this disease. This paper suggests a novel feature learning method for BC diagnosis using the artificial neural network (ANN) by optimizing the hidden layers. It uses a systematic hyper-parameter search method to optimize number of hidden layers, neurons in the hidden layers, activation functions, learning rate, batch size, and training epochs of the ANN. The number of hidden layers and the number of neurons in the hidden layer are investigated using t-distributed stochastic neighbor embedding (t-SNE) technique to specify the best hyper-parameters that achieved the highest testing accuracy while avoiding model overfitting. Comparative analysis showed that the proposed method achieved higher performance than the ANN-based models that utilizes particle swarm optimization (PSO), multi-verse optimizer (MVO), BAT algorithm, and firefly algorithm (FFA) as feature selection (FS) methods, with the best classification accuracy of 0.9948, sensitivity of 0.9815, and specificity of 0.9882 for 10 iterations of holdout cross-validation. The performance of the proposed method is also comparable with several earlier reported state-of-the-art models in the literature. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ANN Brest cancer Classification Feature selection Machine learning

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Games for Scrum Team Collaboration in the Global Software Development Environment: A Literature Review 7

Games for Scrum Team Collaboration in the Global Software De...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Hidayati, Anita Ismail, Iklima Ermis Yuly, Ade Rahma Edison, Henry Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia Blekinge Institute of Technology Department of Software Engineering Sweden

ISBN: (纸本)9798350345711

Software development companies commonly use Global Software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies. © 2022 IEEE.

关键词： Software design

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Novel vision transformer and data augmentation technique for efficient detection of monkeypox disease

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Multimedia Tools and Applications 2024年 1-19页

作者： Alarfaj, Aisha Ahmed Ahmad, Salman Hakeem, Abeer M. Alabdulqader, Ebtisam Abdullah PERO, Chiara Alsubai, Shtwai Innab, Nisreen Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Post Graduate Resident Department of Urology Allama Iqbal Teaching Hospital Dera Ghazi Khan Pakistan Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University P. O. Box 800 Riyadh11421 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Via Giovanni Paolo II 132 Fisciano Salerno Italy Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh13713 Saudi Arabia Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Recent technological advancements have paved the way for the optimization of medical processes, particularly automated disease detection. Moreover, the adoption of machine learning (ML) has greatly helped in automating disease detection. Such approaches can detect various diseases early, enabling timely treatment to save countless lives. Early and accurate diagnosis is very important for diseases like monkeypox, to curb its spread. Monkeypox is a viral disease caused by double-stranded DNA and can be transmitted through close contact with infected humans or animals. It’s early identification and accurate lesion diagnosis are critical to contain the disease. This study proposes an automated approach to optimize the diagnosis of monkeypox disease using a novel vision transformer, which is utilized due to its effectiveness for feature extraction. The Proposed approach’s efficiency and accuracy are tested on a public benchmark dataset comprising a variety of skin lesions of different ages and genders. In addition, data augmentation involves rotation, scaling, and flipping thereby enhancing the density of the training data set for better generalization of ML models. Experiments involve binary, as well as, multi-class classification. For the binary class, the proposed model achieves an accuracy of 97.63%, outperforming traditional ML and deep learning (DL) techniques. In the case of multi-class classification with monkeypox, measles, normal, HFMD, cowpox, and chickenpox classes, the proposed model archives an accuracy of 90.61% while precision, recall, and F1 scores are 91.39%, 89.17%, and 90.28%, respectively. Furthermore, the proposed approach shows average accuracy, precision, recall, and F1 scores of 97.54%, 96.19%, 95.16%, and 95.67%, respectively for five-fold cross-validation. Experiments demonstrate that the combination of data augmentation techniques and the vision transformer model significantly optimizes diagnostic performance. In brief, advanced DL architectur

关键词： Medical image processing

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A Yet Powerful Routing Protocol for Vehicular Ad-hoc Networks (VANETs) Utilizing Software Defined Network (SDN) and Fog Computing

A Yet Powerful Routing Protocol for Vehicular Ad-hoc Network...

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International Conference on Information Technology, Information systems and Electrical engineering (ICITISEE)

作者： Khalid A. Darabkh Mamoun F. Al-Mistarihi Mera Ismail Al-Maaitah Computer Engineering Department School of Engineering The University of Jordan Amman Jordan Electrical Engineering Department Faculty of Engineering Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

In this article, we propose a novel protocol that employs different technologies together, such as Software Defined Network (SDN) for the purpose of offering scalability, programmability, and global network information, and Fog computing for the reason of providing location services that meet V ANET standards. Additionally, we propose a novel cluster head selection algorithm for the purpose of enhancing the efficiency of the protocol by considering three different dimensions, namely, lifetime, average distance, and signal-to-interference noise ratio. The novelty of this proposed algorithm comes from reducing the number of control overheads, reducing long-distance communications, and reducing packet loss due to collisions and contentions inside each cluster, respectively. The proposed protocol has been assessed using N s3 simulator whereas its results are so promising and worth publishing.

关键词： Simulation Software algorithms Clustering algorithms Packet loss computer architecture Throughput Routing protocols Software defined networking Standards Edge computing

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Evaluation of importance for condition attributes based on quality of decision reducts 26

Evaluation of importance for condition attributes based on q...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering systems, KES 2022

作者： Stanczyk, Urszula Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Relative or decision reducts belong with mechanisms dedicated to feature selection, and they are embedded in rough set approach to data processing. Algorithms for reduct construction typically aim at dimensionality reduction aspect, searching for smallest reducts, which are considered as the most advantageous from the point of view of knowledge representation. However, classifiers build on reduced data models, based on reducts, can significantly vary in performance. Therefore, to ensure quality of predictions, other characteristics of reducts, apart from their cardinalities, need to be taken into account. The paper presents research in which estimation of reduct quality through their characteristics was reflected in calculation of the proposed weighting factors leading to attribute rankings. These rankings were next employed in the process of filtering decision rules, inferred by classic rough set approach. Constructed rule-based classifiers were applied in the stylometric domain to solve a task of authorship attribution. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & engineering systems (KES 2022)

关键词： Rough set theory

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Comparative Analysis of Edge Detection Algorithms with Particle Swarm Optimization for Enhanced Image Processing

Comparative Analysis of Edge Detection Algorithms with Parti...

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International Conference on ICT For Smart Society (ICISS)

作者： Sri Rahayu Pulung Nurtantio Andono Pujiono Ahmad Zainul Fanani Department of Informatics Engineering Faculty of Computer Science Dian Nuswantoro University Semarang Indonesia Computer Science Department Institut Teknologi Garut Garut Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Edge detection plays an important role in various fields by identifying object boundaries and supporting advanced image analysis, such as segmentation, recognition, and tracking. Many edge detection algorithms, such as Canny, Laplacian, and Prewitt, have been developed to address issues such as noise sensitivity, computational complexity, and detection accuracy. This research compares the optimized algorithms using Particle Swarm Optimization (PSO). The results of this study show that the optimized algorithm provides better performance based on the evaluation conducted using entropy, Mean Squared Error (MSE), and computation time, and has practical implications. This research also determines the most effective edge detection technique in various image processing scenarios, thus contributing to the optimization of image analysis workflows in real-world contexts.

关键词： Accuracy Laplace equations Image analysis Image edge detection Noise Prediction algorithms Entropy Computational efficiency Particle swarm optimization Optimization

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