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SSRN 2022年

作者： Aboud, Ahlem Rokbani, Nizar Fdhila, Raja Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Hussain, Amir Alimi, Adel M. University of Sousse ISITCom Sousse4011 Tunisia BP 1173 Sfax3038 Tunisia High Institute of Applied Science and Technology of Sousse University of Sousse Tunisia Department of Computer Science College of Computers and Information Technology Taif University P.O.Box. 11099 Taif21944 Saudi Arabia College of Applied Computer Science King Saud University Riyadh Saudi Arabia Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Particle Swarm Optimization (PSO) system based on the distributed architecture over multiple sub-swarms is very efficient for static multi-objective optimization, but has not been considered for solving dynamic multi-objective problems (DMOPs). Tracking the most effective solutions over time and ensuring good exploitation and exploration are the main challenges of solving DMOP. This study proposes a Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable a multiple sub-swarm’ subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. The DPb-MOPSO system is tested on a set of DMOPs with different types of time-varying Pareto Optimal Set (POS) and Pareto Optimal Front (POF). Inverted generational distance (IGD), mean inverted generational distance (MIGD), and hypervolume difference (HVD) metrics are used to assess the DPb-MOPSO performances. Quantitative results are analyzed using Friedman's analysis of variance, while stability is analyzed using Lyapunov's theorem. The DPb-MOPSO is more robust than several dynamic multi-objective evolutionary algorithms in solving 21 complex problems over a range of changes in both the POS and POF. On IGD and HVD, DPb-MOPSO can solve 8/13 and 7/13 of the 13 UDF and ZJZ functions with moderate changes. DPb-MOPSO is able to resolve 4/8 FDA and DMOP benchmarks with severe changes to the MIGD, and 5/8 with moderate changes. DPb-MOPSO assumes 4/8 of the solving function on IGD

关键词： Particle swarm optimization (PSO)

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Computer development based embedded systems in precision agriculture: tools and application

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Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 2022年第1期72卷 589-611页

作者： Amine Saddik Rachid Latif Abdelhafid El Ouardi Mohamed Elhoseny Adel Khelifi a Laboratory of Systems Engineering and Information Technology LISTI National School of Applied Sciences Ibn Zohr University Agadir Agadir Morocco b Université Paris-Saclay ENS Paris-Saclay CNRS Systèmes et Applications des Technologies de l'Information et de l'Energie Orsay France c College of Computing and Informatics University of Sharjah Dubai UAEd Faculty of Computers and Information Mansoura University Mansoura Egypt e Software Engineering Abu Dhabi University Abu Dhabi UAE

ABSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to *** agriculture (PA) research aims to design decision systems based on agricultural site control and management. These systems consist of observing fields and measuring metrics to optimize yields and investments while preserving resources. The corresponding applications can be found on large agricultural areas based on satellites, unmanned aerial vehicles (UAVs), and sol robots. All these applications based on various algorithms that are complex in terms of processing time. If these algorithms are evaluated offline on work-stations or desktops, this is not the case for algorithms that need to be embedded and should operate and help make real-time decisions. We, therefore, need an advanced study using hardware-software co-design approach to design decision systems to embed different algorithms, including sensor data acquisition and processing units. In this work, we propose a review in processing information tools-based embedded systems in PA algorithms with different applications: weed detection, numerical counting, monitoring of plant indexes, and disease detection. This review has been based on more than 100 papers to extract useful information on the different techniques used and the information processing systems. The elaborated study presents the various tools, databases, and systems in order to extract the advantages and disadvantages of system and application.

关键词： : Information development precision agriculture satellite unmanned aerial vehicles sol robots algorithms greenhous H-S co-design embedded system databases

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A New Concept of Bio-based Prestress technology with Experimental Proof-of-Concept on Bamboo-Timber Composite Beams

Research Square

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Research Square 2023年

作者： Zhang, Hexin Shen, Minhe Deng, Yu Andras, Peter Sukontasukkul, Piti Yuen, Terry Y.P. Tang, Yunchao Wong, Simon H.F. Limkatanyu, Suchart Singleton, Ian Hansapinyo, Chayanon School of Computing Engineering and the Built Environment School of Applied Sciences Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom School of Civil Engineering and Architecture Guangxi University of Science and Technology Liuzhou545006 China Construction and Building Materials Research Center Department of Civil Engineering King Mongkut’s University of Technology North Bangkok Thailand Department of Civil Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan College of Urban Construction Zhongkai University of Agriculture and Engineering Guangzhou510225 China Faculty of Science and Technology Technological Higher Education Institute of Hong Kong 20A Tsing Yi Road Tsing Yi Island New Territories Hong Kong Department of Civil and Environmental Engineering Faculty of Engineering Prince of Songkla University Songkhla90112 Thailand Center of Excellence for Natural Disaster Management Department of Civil Engineering Faculty of Engineering Chiang Mai University Thailand

This paper presents a pioneering experimental proof-of-concept study to validate a novel concept of prestress technology that used only pure bio-based composite materials while achieved consistent prestressed stress distribution within the structure member, and provided in-situ flexibility, improved structural performance, and maximised the rate of utilisation of each material. Industrial level of facilities were used during this development. The prestress is achieved by pressurised/forced lamination of multiple components with different materials and geometrical properties. The prestressing process is activated during the pressure release stage during which the components are interacting with each other, creating different stress statuses that would favour the weaker and adverse the stronger components to maximise the strength exploitation of different materials. Using laminated bamboo and timber as an example pair, twenty-two glulam, non-prestressed and prestressed laminated bamboo-timber composite beams were manufactured, tested, and analysed to provide an in-depth understanding of the structural behaviours of these novel structural members. Failure modes, yielding, ultimate and serviceability limit loads, and corresponding deflections, as well as the histories of strain development at key positions of the specimens were examined. The experimental study confirmed the feasibility, effectiveness and industrial scalability of the proposed technology. The novel concept provides a new approach for developing the prestress technology for bio-based materials, and this experimental study laid the foundation for its future analytical development and numerical studies. © 2023, CC BY.

关键词： Laminated composites

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An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis

arXiv

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arXiv 2023年

作者： Han, Luyi Zhang, Tianyu Huang, Yunzhi Dou, Haoran Wang, Xin Gao, Yuan Lu, Chunyao Tan, Tao Mann, Ritse Department of Radiology and Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands Amsterdam Netherlands GROW School for Oncology and Development Biology Maastricht University P. O. Box 616 Maastricht6200 MD Netherlands Faculty of Applied Science Macao Polytechnic University Macao999078 China Institute for AI in Medicine School of Automation Nanjing University of Information Science and Technology Nanjing China School of Computing University of Leeds Leeds United Kingdom School of Medicine University of Leeds Leeds United Kingdom

Multi-sequence MRI is valuable in clinical settings for reliable diagnosis and treatment prognosis, but some sequences may be unusable or missing for various reasons. To address this issue, MRI synthesis is a potential solution. Recent deep learning-based methods have achieved good performance in combining multiple available sequences for missing sequence synthesis. Despite their success, these methods lack the ability to quantify the contributions of different input sequences and estimate the quality of generated images, making it hard to be practical. Hence, we propose an explainable task-specific synthesis network, which adapts weights automatically for specific sequence generation tasks and provides interpretability and reliability from two sides: (1) visualize the contribution of each input sequence in the fusion stage by a trainable task-specific weighted average module;(2) highlight the area the network tried to refine during synthesizing by a task-specific attention module. We conduct experiments on the BraTS2021 dataset of 1251 subjects, and results on arbitrary sequence synthesis indicate that the proposed method achieves better performance than the state-of-the-art methods. Our code is available at https://***/fiy2W/mri_seq2seq. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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Journal of Healthcare Engineering 2022年第1期2022卷 3861161页

作者： Alzu'Bi, Dalia Abdullah, Malak Hmeidi, Ismail Alazab, Rami Gharaibeh, Maha El-Heis, Mwaffaq Almotairi, Khaled H. Forestiero, Agostino Hussein, Ahmad Mohdaziz Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid2210 Jordan Department of General Surgery and Urology University of Science and Technology Irbid22110 Jordan Department of Diagnostic and Interventional Radiology Faculty of Medicine Jordan University of Science and Technology Irbid2210 Jordan Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah21955 Saudi Arabia Institute for High Performance Computing and Networking CNR CS Rende Italy Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah21955 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models;three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results;they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life. © 2022 Dalia Alzu'bi et al.

关键词： Convolutional neural networks

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6LoWPAN Protocol in Fixed Environment: A Performance Assessment Analysis 10

6LoWPAN Protocol in Fixed Environment: A Performance Assessm...

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10th IEEE International Conference on Intelligent Data Acquisition and Advanced computing Systems: technology and Applications, IDAACS 2019

作者： Mohd Yusoff, Nin Hayati Zakaria, Nurul Azma Sikora, Axel Sebastian, Jubin S. Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Malaysia Center for Advanced Computing Technology Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Malaysia Offenburg University of Applied Sciences Offenburg D-77652 Germany

ISBN: (纸本)9781728140681

The Internet of Things (IoT) application has becoming progressively in-demand, most notably for the embedded devices (ED). However, each device has its own difference in computational capabilities, memory usage, and energy resources in connecting to the Internet by using Wireless Sensor Networks (WSNs). In order for this to be achievable, the WSNs that form the bulk of the IoT implementation requires a new set of technologies and protocol that would have a defined area, in which it addresses. Thus, IPv6 Low Power Area Network (6LoWPAN) was designed by the Internet Engineering Task Force (IETF) as a standard network for ED. Nevertheless, the communication between ED and 6LoWPAN requires appropriate routing protocols for it to achieve the efficient Quality of Service (QoS). Among the protocols of 6LoWPAN network, RPL is considered to be the best protocol, however its Energy Consumption (EC) and Routing Overhead (RO) is considerably high when it is implemented in a large network. Therefore, this paper would propose the HRPL to enchance the RPL protocol in reducing the EC and RO. In this study, the researchers would present the performance of RPL and HRPL in terms of EC, Control traffic Overhead (CTO) and latency based on the simulation of the 6LoWPAN network in fixed environment using COOJA simulator. The results show HRPL protocol achieves better performance in all the tested topology in terms of EC and CTO. However, the latency of HRPL only improves in chain topology compared with RPL. We found that further research is required to study the relationship between the latency and the load of packet transmission in order to optimize the EC usage. © 2019 IEEE.

关键词： Energy utilization

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Exploring the Elastic Properties and Fracture Patterns of Me-Graphene Monolayers and Nanotubes through Reactive Molecular Dynamics Simulations

arXiv

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arXiv 2023年

作者： Pereira Junior, Marcelo L. De Sousa, José M. Brandão, Wjefferson H.S. Galvão, Douglas S. Fonseca, Alexandre F. Ribeiro Junior, Luiz A. University of Brasília Faculty of Technology Department of Electrical Engineering Brasília Brazil Federal Institute of Education Science and Technology of Piauí – IFPI Piauí São Raimundo Notato 64770-000 Brazil Applied Physics Department "Gleb Wataghin" Institute of Physics - IFGW University of Campinas – UNICAMP Rua Sérgio Buarque de Holanda 777 - Cidade Universitária São Paulo Campinas13083-859 Brazil Department of Physics Federal University of Piauí Piauí Terezina Brazil Center for Computing in Engineering & Sciences Unicamp SP Campinas Brazil University of Brasília Institute of Physics Brasília Brazil

Me-graphene (MeG) is a novel two-dimensional (2D) carbon allotrope. Due to its attractive electronic and structural properties, it is important to study the mechanical behavior of MeG in its monolayer and nanotube topologies. In this work, we conducted fully atomistic reactive molecular dynamics simulations using the Tersoff force field to investigate their mechanical properties and fracture patterns. Our results indicate that Young’s modulus of MeG monolayers is about 414 GPa and in the range of 421-483 GPa for the nanotubes investigated here. MeG monolayers and MeGNTs directly undergo from elastic to complete fracture under critical strain without a plastic *** Codes 00-xx © 2023, CC BY.

关键词： Graphene

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Automatic Detection of Breast Cancer in Thermal Images using Optimal Handcrafted Features

Automatic Detection of Breast Cancer in Thermal Images using...

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International Conference on Computer Theory and Applications (ICCTA)

作者： Seifedine Kadry Venkatesan Rajinikanth Susmitha Dasari T. Suvathi Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai India Department of Computer Science and Engineering St. Joseph's College of Engineering OMR Chennai India

ISBN: (数字)9781665478540

ISBN: (纸本)9781665478557

Breast Cancer (BC) is one of the harsh illnesses which widely distress the women population and premature recognition is always preferred to reduce its harshness. BC is normally detected in hospitals using a chosen non-invasive medical imaging technique. The proposed work aims to develop and execute the machine learning supported automatic detection of the BC using breast Thermal Images (TI). In TI, examination of thermal silhouette helps to get the necessary information about the normal/cancer infected section. The developed system consist the following phases; (i) Image collection and pre-processing, (ii) Image enhancement and handcrafted feature extraction, (iii) Feature selection with Bat Algorithm, and (iv) Binary classification and validation. In this work, a 5-fold cross validation is employed to classify the test images and the detected accuracy of Fine KNN is better (>92%) compared to the alternatives. The proposed work is tested using benchmark breast TI and the outcome of this research confirms its clinical significance.

关键词： Hospitals Sociology Fitting Machine learning Feature extraction Breast cancer Classification algorithms

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The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue

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Medical Image Analysis 2024年 97卷 103257页

作者： Weitz, Philippe Valkonen, Masi Solorzano, Leslie Carr, Circe Kartasalo, Kimmo Boissin, Constance Koivukoski, Sonja Kuusela, Aino Rasic, Dusan Feng, Yanbo Pouplier, Sandra Sinius Sharma, Abhinav Eriksson, Kajsa Ledesma Robertson, Stephanie Marzahl, Christian Gatenbee, Chandler D. Anderson, Alexander R.A. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Budelmann, Daniel Weiss, Nick Heldmann, Stefan Lotz, Johannes Wolterink, Jelmer M. De Santi, Bruno Patil, Abhijeet Sethi, Amit Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Farrokh, Mahtab Kumar, Neeraj Greiner, Russell Latonen, Leena Laenkholm, Anne-Vibeke Hartman, Johan Ruusuvuori, Pekka Rantalainen, Mattias Department of Medical Epidemiology and Biostatistics Karolinska Insitutet Stockholm Sweden Institute of Biomedicine University of Turku Turku Finland Institute of Biomedicine University of Eastern Finland Kuopio Finland Department of Surgical Pathology Zealand University Hospital Roskilde Denmark Department of Oncology and Pathology Karolinska Institutet Stockholm Sweden Gestalt Diagnostics Spokane United States Department of Integrated Mathematical Oncology Moffitt Cancer Center Tampa United States Informatics Institute University of Applied Sciences Western Switzerland Switzerland Department of Measurement and Electronics AGH University of Kraków Poland Department of Computer Science University of Geneva Geneva Switzerland Department of Neuroscience University of Padova Italy Medical Faculty University of Geneva Switzerland Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany Department of Applied Mathematics Technical Medical Centre University of Twente Enschede Netherlands Multimodality Medical Imaging Technical Medical Centre University of Twente Enschede Netherlands Department of Electrical Engineering Indian Institute of Technology Bombay India Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan Faculty of Medical Technology Niigata University of Health and Welfare Niigata Japan FORXAI Business Operations Konica Minolta Inc. Osaka Japan Department of Computing Science University of Alberta EdmontonAlberta United States Alberta Machine Intelligence Institute Edmonton Canada MedTechLabs BioClinicum Karolinska University Hospital Stockholm Sweden Faculty of Medicine and Health Technology Tampere University Tampere Finland

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods. © 2024

关键词： Tissue

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Operational matrix for solving fractional differential equations with Erdelyi-Kober differential operator

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AIP Conference Proceedings 2024年第1期2905卷

作者： Farah Suraya Md Nasrudin Chang Phang Shafaruniza Mahadi Norazam Arbin Mathematical Sciences Srudies College of Computing Informatics and Media Universiti Teknologi MARA Johor Branch Segamat Campus Segamat 85000 Malaysia Department of Mathematics and Statistics Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Kampus Pagoh Pagoh 84600 Malaysia

The operational matrix based on shifted Legendre polynomials is used in this study to solve Erdelyi-Kober (E-K) fractional differential equations. To do this, we derive the analytical expression for polynomial for E-K derivative of polynomial xk where k is positive integer. As a result, the operational matrix approach for the E-K derivative is developed by employing the characteristics of shifted Legendre polynomials for the first time here. The E-K fractional differential equations are then converted into a system of algebraic equations. We can acquire the numerical solution to fractional differential equations defined in the E-K derivative by solving that system. We simply need a small number of terms of shifted Legendre polynomials to get the accurate solution.

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