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arXiv 2023年

作者： Saifullah, Shoffan Drezewski, Rafal Yudhana, Anton Pranolo, Andri Kaswijanti, Wilis Suryotomo, Andiko Putro Putra, Seno Aji Khaliduzzaman, Alin Prabuwono, Anton Satria Japkowicz, Nathalie Institute of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Center of Knowledge Engineering and Data Science Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang65145 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia College of Computer and Information Hohai University Nanjing211100 China Department of Informatics Faculty of Industrial Technology Universitas Ahmad Dahlan Yogyakarta55166 Indonesia Faculty of Agricultural Engineering and Technology Sylhet Agricultural University Sylhet3100 Bangladesh Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science American University WashingtonDC20016 United States

This study explored the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection for precise poultry hatchery practices. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset (200 single egg images) using augmented images (rotation, flip, scale, translation, and reflection). The training results demonstrated that all models achieved high accuracy, indicating their ability to learn and classify chicken eggs’ fertility. However, variations in accuracy and performance were observed when these models were evaluated on the testing datasets. The InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. It demonstrated excellent performance in all parameters of the evaluation metrics for both training and testing datasets. When evaluated on the testing datasets, it achieved an accuracy of 0.98, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.96 for identifying non-fertile eggs. The higher performance is attributed to its unique architecture, efficiently capturing features at different scales, which leads to improved accuracy and robustness. Further optimization and fine-tuning of the models might be necessary to address the limitations in accurately detecting fertile and non-fertile eggs using other models. This study highlighted the potential of CNN-transfer learning for nondestructive fertility detection and emphasized the need for further research to enhance the models’ capabilities and to ensure accurate *** Codes CS-Class: 68T07, 68T45, 68U10, ICT-Class: 94A08 © 2023, CC BY-SA.

关键词： Image classification

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Reconfigurable intelligent surfaces for wireless communications:Overview of hardware designs,channel models,and estimation techniques

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Intelligent and Converged Networks 2022年第1期3卷 1-32页

作者： Mengnan Jian George C.Alexandropoulos Ertugrul Basar Chongwen Huang Ruiqi Liu Yuanwei Liu Chau Yuen the Wireless Research Institute ZTE CorporationBeijing 100029China the State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China Department of Informatics and Telecommunications National and Kapodistrian University of AthensAthens 15784Greece Communications Research and Innovation Laboratory(CoreLab) Department of Electrical and Electronics EngineeringKoçUniversityIstanbul 34450Turkey College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK. the Engineering Product Development(EPD)Pillar Singapore University of Technology and DesignSingapore 487372Singapore

The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing *** the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of *** RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field *** by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal *** also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless ***,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

关键词： reconfigurable intelligent surfaces beyond 5G 6G channel modeling hardware architecture channel estimation smart wireless environment standardization

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Modeling Prototypical Preference Behavior and Diversity using Rank Score Characteristic Functions 21

Modeling Prototypical Preference Behavior and Diversity usin...

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21st IEEE International Conference on Cognitive informatics and Cognitive Computing, ICCI*CC 2022

作者： Schweikert, Christina Shimojo, Shinsuke Glasser, Hannah Hendsey, Rebecca Alsaber, Rami Hsu, D. Frank St. John's University QueensNY United States California Institute of Technology Division of Biology and Biological Engineering / Computation and Neural Systems PasadenaCA United States Fordham University Laboratory of Informatics and Data Mining Department of Computer and Information Science New YorkNY United States

ISBN: (纸本)9781665490849

When given two human face images to choose, a subject's decision process is recorded as a sequence of eye movement gaze points. This sequence is then analyzed to detect and predict the preference made by the subject. In an experiment with twelve subjects, each with sixty trials, we have analyzed the 720 sequences using five attributes and combinatorial fusion. Results are promising with good accuracy and efficiency. In this paper, we characterize the decision-making behavior of each subject and measure the cognitive diversity between each of these twelve subjects and a prototypical subject. Our study contributes to improving the data and predictive quality of the experiment and the computational modeling. © 2022 IEEE.

关键词： Eye movements

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CNN segmentation of skin melanoma in pre-processed dermoscopy images

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Procedia computer Science 2024年 235卷 2775-2782页

作者： Seifedine Kadry Elena Verdú Robertas Damasevicius Laith Abualigah Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu’s thresholding, (iii) Kapur’s thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database.

关键词： Watershed-algorithm skin melanoma segmentation Fuzzy-Tsallis entropy Moth-Flame algorithm evaluation

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Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

SSRN

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SSRN 2024年

作者： Wang, Zhiyuan Duan, Jinhao Yuan, Chenxi Chen, Qingyu Chen, Tianlong Yao, Huaxiu Zhang, Yue Wang, Ren Xu, Kaidi Shi, Xiaoshuang Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Computer Science Drexel University PhiladelphiaPA19104 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Section of Biomedical Informatics & Data Science Yale School of Medicine Yale University New HavenCT06510 United States Department of Computer Science University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence interaction systems, particularly in the healthcare engineering domain. However, a general method for quantifying the uncertainty of free-form answers has yet to be established in open-ended medical question-answering (QA) tasks, where irrelevant words and sequences with limited semantic information can be the primary source of uncertainty due to the presence of generative inequality. In this paper, we propose the Word-Sequence Entropy (WSE ), which calibrates the uncertainty proportion at both the word and sequence levels according to the semantic relevance, with greater emphasis placed on keywords and more relevant sequences when performing uncertainty quantification. We compare WSE with 6 baseline methods on 5 free-form medical QA datasets, utilizing 7 "off-the-shelf" large language models (LLMs), and show that WSE exhibits superior performance on accurate uncertainty measurement under two standard criteria for correctness evaluation. Additionally, in terms of the potential for real-world medical QA applications, we achieve a significant enhancement (e.g., +6.36% improvement of model accuracy on the COVID-QA dataset) in the performance of LLMs when employing sequences with lower uncertainty that are identified by WSE as final answers, without requiring additional task-specific fine-tuning or architectural modifications. © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions

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Procedia computer Science 2024年 235卷 3438-3446页

作者： Seifedine Kadry Laith Abualigah Rubén González Crespo Elena Verdú Robertas Damasevicius Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays.

关键词： Covid19 X-ray Vision-transformer Patch-size Classification

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Measurement of Synthetic-Dimension Band Structures in Silicon Coupled Ring Resonators

Measurement of Synthetic-Dimension Band Structures in Silico...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Ruming Zhang Liucun Li Mikiya Kamata Toshihiko Baba Tomoki Ozawa Yasutomo Ota Satoshi Iwamoto Research Center for Advanced Science and Technology The University of Tokyo Tokyo Japan Department of Electrical and Computer Engineering Yokohama National University Yokohama Japan Advanced Institute for Materials Research Tohoku University Sendai Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Japan

We fabricated integrated silicon coupled-ring resonators and measured photonic band structures for synthetic frequency lattices with different configurations. A phase difference between two modulation signals introduc... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Band structures Phase modulation Resonant frequency Measurement by laser beam Ring lasers Silicon Frequency measurement

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Modelling and Use of Adaptive Control Using Petri Nets

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Procedia computer Science 2023年 225卷 1438-1447页

作者： Zoltán Balogh Kristián Fodor Jan Francisti Martin Drozda Maroš Čavojský Department of Computer Science Constantine the Philosopher University in Nitra 949 01 Nitra Slovakia Kándó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary Institute of Informatics and Mathematics Slovak University of Technology in Bratislava 811 07 Bratislava Slovakia

The issue of modeling holds significant importance for the applicability of control theory, particularly at higher control levels where the verbal description of control objects becomes increasingly crucial. However, a notable disadvantage of such verbal descriptions is the inherent ambiguity and vagueness of the obtained results. This article aims to address this challenge by proposing an approach that utilizes Petri nets (PN) to model and control a specific component within an intelligent home. Additionally, the Simulink modeling tool and Petri net analysis tools are introduced as aids in the modeling process. The article focuses on creating a model that accurately represents the temperature regulation system in the home, considering the constant rise in energy consumption and costs. Furthermore, the transfer of the model to the Simulink environment using IF THEN rules is demonstrated, allowing for an evaluation of its performance. Through the comprehensive application of this approach, the aim is to optimize energy usage and reduce costs within household environments.

关键词： Petri nets Simulation Matlab Smart homes

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Automatic liver-vessel examination from CT slice using Kapur’s thresholding and watershed algorithm

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Procedia computer Science 2024年 235卷 1824-1831页

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur’s tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme.

关键词： liver CT liver-vessel Kapur’s thresholding hummingbird-optimizer watershed-algorithm

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Access Control Design Based on User Role Type in Telemedicine System Using Ethereum Blockchain 21

Access Control Design Based on User Role Type in Telemedicin...

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Proceedings of the 7th International Conference on Communication and Information Processing

作者： Muhammad Hamzah Diyanatul Husna Fransiskus Astha Ekadiyanto Ketut Purnama Afif Nurul Hidayati Mauridhi Hery Purnomo Supeno Mardi Susiki Nugroho Reza Fuad Rachmadi Ingrid Nurtanio Anak Agung Ratna Department of Electrical Engineering Universitas Indonesia Indonesia Department of Computer Engineering Sepuluh November Institute of Technology Indonesia Department of Dermatology and Venereology Airlangga University Indonesia Department of Informatics Hassanudin University Indonesia

ISBN: (纸本)9781450385190

To fulfill health as basic human needs, health care services technology is always improved but also must maintain security and privacy of the data due to huge amount of data is created and distributed. Telemedicine with telecommunication technology offers convenience and cheaper cost, but still is vulnerable to cyber attacks, making it a threat for patients’ data's privacy. One approach that can be used to secure the data is applying access control to the data. Immutable blockchain can helps to enforce access control so it cannot be violated. We propose a system which implements Ethereum for the blockchain and React web application for the interface of the system. Data management and the access control are provided through Ethereum smart contracts, and the access control requires different role-based permission to upload and access the patients’ data. The system works successfully with average time taken to verify the role is 1.8033 seconds per session. The privacy of the patients’ data is ensured because only the patients that are allowed to keep the ID of their own data.

关键词： Blockchain Web Smart Contract Telemedicine User Role

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