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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Amadea, Avissa Aurellia Sari, Christy Atika Rachmawanto, Eko Hari Sari, Wellia Shinta Noori, Mohammed S. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Based on WHO's data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient's diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection. © 2023 IEEE.

关键词： Diseases

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Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images

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Physical Review Applied 2025年第3期23卷 034035-034035页

作者： Niko R. Reed Danyal Bhutto Matthew J. Turner Declan M. Daly Sean M. Oliver Jiashen Tang Kevin S. Olsson Nicholas Langellier Mark J.H. Ku Department of Physics University of Maryland College Park Maryland USA Quantum Technology Center University of Maryland College Park Maryland USA Joint Quantum Institute University of Maryland College Park Maryland USA Department of Biomedical Engineering Boston University Boston Massachusetts USA A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA Department of Electrical and Computer Engineering University of Maryland College Park Maryland USA The MITRE Corporation McLean Virginia 22102 USA Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College Park Maryland 20742 USA Department of Physics Harvard University Cambridge Massachusetts USA Present address: VideaHealth Inc. Boston MA. Department of Materials Science and Engineering University of Delaware Newark Delaware 19716 USA Department of Physics and Astronomy University of Delaware Newark Delaware 19716 USA Present address: Northrop Grumman Mission Systems Linthicum Maryland 21090 USA.

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high-spatial-frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional images of vector magnetic fields acquired by a quantum diamond microscope . Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower-signal-to-noise-ratio data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources.

关键词： Signal to noise ratio

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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Development of hydroxyapatite-enhanced membrane for nanoplastics removal: multiple scenarios and mechanism exploration

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Journal of Hazardous Materials 2025年

作者： Mengna Li Guohe Huang Shuguang Wang Jing Huang Zeyuan Xu Environmental Systems Engineering Program University of Regina Regina Saskatchewan S4S 0A2 Canada China-Canada Center of Energy Environment and Sustainability Research UR-SDU School of Environmental Science and Engineering Shandong University Qingdao 266237 China Shandong Key Laboratory of Water Pollution Control and Resource Reuse School of Environmental Science and Engineering Shandong University Qingdao 266237 China Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia Italy

Nanoplastics (NPs) are widespread in wastewater systems and more toxic than microplastics (MPs), necessitating effective removal techniques. Although membranes can effectively remove MPs, their efficiency and mechanisms for NPs removal require further study. A novel hydroxyapatite functionalized PVDF (HAPF) membrane was developed for NPs removal. The optimized HAPF membrane was prepared via a one-step method with 800 mg/L HAP at pH 7.3 for 8 hours. It achieved a water flux of 4376.44 LMH, 3.4 times higher than that of the pristine PVDF membrane, while maintaining a polystyrene (PS) NPs rejection above 99.5%. The HAPF membrane maintained 95% of its pure water flux when treating wastewater with only PS NPs, but exhibited severe flux decline under varying conditions, such as surface-functionalized PS NPs, pH changes, or coexisting contaminants. Among these, the presence of MgSO 4 alongside PS NPs caused the most severe water flux reduction, with the HAPF membrane undergoing complete blocking, followed by the formation of a cake layer. XDLVO results revealed that there is an attractive interaction between the HAPF membrane and PS NPs, primarily driven by acid-base interaction. The HAPF membrane exhibited significantly lower R m and R f values than the PVDF membrane and achieved a flux recovery rate of 92.94% over multiple fouling-cleaning cycles. Its water flux was 18.9 times that of the PVDF membrane during long-term filtration. The developed HAPF membrane holds significant potential for advanced water treatment and fouling.

关键词： Hydroxyapatite functionalized PVDF (HAPF) membrane nanoplastic removal coexisting pollutants XDLVO theory Hermia theory

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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Large language models for diabetes training:a prospective study

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science Bulletin 2025年第6期70卷 934-942页

作者： Haoxuan Li Zehua Jiang Zhouyu Guan Yuqian Bao Yuexing Liu Tingting Hu Jiajia Li Ruhan Liu Liang Wu Di Cheng Hongwei Ji Yong Wang Ya-Xing Wang Carol Y.Cheung Yingfeng Zheng Jihong Wang Zhen Li Weibing Wu Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan Ching-Yu Cheng Tien Yin Wong Huating Li Chun Cai Lijuan Mao Lee-Ling Lim Yih-Chung Tham Bin Sheng Weiping Jia Shanghai University of Sport Shanghai 200438China School of Clinical Medicine Beijing Tsinghua Changgung HospitalTsinghua MedicineTsinghua UniversityBeijing 100084China Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua MedicineTsinghua UniversityBeijing 100084China Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MoE Key Lab of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China CEMS NCMISHCMSMADISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100049China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China k.Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Department of Renal Medicine Singapore General HospitalSingHealth-Duke Academic Medical CentreSingapore169

Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes *** Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like *** first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United ***,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical *** found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and *** the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B ***-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.

关键词： Diabetes Diabetestraining Largelanguagemodels Primarydiabetescare Prospectivestudy

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Automating the reconfiguration for self-adaptable software

Automating the reconfiguration for self-adaptable software

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17th International Doctoral Symposium on Components and Architecture, WCOP'12

作者： Di Beneditto, Marco Eugênio Madeira Systems Engineering and Computer Science Program Federal University of Rio de Janeiro PO Box: 68511 21945-970 Rio de Janeiro RJ Brazil

ISBN: (纸本)9781450313483

Considering a runtime compositional software adaptation, one important aspect about the engineering of self-adaptive systems is to decide how to reconfigure the software, that is, how to change its structure. After a desired configuration has been chosen for some context, the task is to decide how to achieve this desired state and, after that, execute this reconfiguration. Furthermore, this reconfiguration plan must be correct in relation to the programming model, the application architectural restrictions and, must leave the application in a consistent state. The plan execution must guarantee some level of reliability not to bring the system in an undesired state. In order to automate the reconfiguration, this work proposes an approach that employs Artificial Intelligence techniques to solve this issue. The reconfiguration plans are based on a model of cause and effect, which describes the set of reconfiguration actions from the underlying component model and, this component model provides a reflective support to execute the generated reconfiguration plan. Copyright © 2012 ACM.

关键词： Software architecture

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A reconfigurable ray-tracing Multi-Processor SoC with hardware replication-aware instruction set extension

A reconfigurable ray-tracing Multi-Processor SoC with hardwa...

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13th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2013

作者： Nery, Alexandre S. Nedjah, Nadia França, Felipe M.G. Jozwiak, Lech Corporaal, Henk LAM - Computer Architecture and Microeletronics Laboratory Systems Engineering and Computer Science Program COPPE Federal University of Rio de Janeiro Brazil Department of Electronics Engineering and Telecommunications Faculty of Engineering State University of Rio de Janeiro Brazil Department of Electrical Engineering - Electronic Systems Eindhoven University of Technology Netherlands

ISBN: (纸本)9783319038582

Application code and processor parallelization, together with instruction set customization, are the most common and effective ways to enhance the performance and efficiency of application-specific processors (ASIPs). Both the effective code parallelization and data/task parallelism exploitation, as well as effective instruction set customization, enable an ASIP to achieve a significant performance improvement using limited extra hardware resources. However, a naive parallelization or instruction set customization may not result in the required performance improvement, leading to a waste of computing and energy resources. Therefore, when performing parallelization or custom instruction selection, complex tradeoffs between processing speed, circuit area and power consumption must be closely observed. In this paper, we propose and discuss an efficient ASIP-based Multi-Processor System-on-a-Chip (MPSoC) design for ray-tracing, exploiting application parallelism and hardware replication-aware instruction set customization. Without hardware sharing among the custom instructions units, the proposed parallel ray-tracer MPSoC design with custom instructions achieves 77% speed up in comparison to a single microprocessor design with the default instruction set. However, with the replication-aware instruction set customization, the speed up increases to 81%. © Springer International Publishing Switzerland 2013.

关键词： Integrated circuit design

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Instant Messaging as Information Goldmines to Digital Forensic: A Systematic Review 3

Instant Messaging as Information Goldmines to Digital Forens...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Sihombing, Harry Chandra Fajar, Ahmad Nurul Utama, Ditdit Nugeraha Mercu Buana University Master of Electrical Engineering Jakarta Indonesia Information Systems Management Department Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Jakarta11480 Indonesia Computer Science Department Bina Nusantara University BINUS Graduate Program-Master of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

The use of instant messaging (IM) application is very communal among smartphone users, as it delivers ease in interaction and communication. The speed at which technologies, growth, and expansion of networks infrastructure for high-speed data networks (e.g. wifi, 4G, etc.) are changing and the high number of IM applications are available. IM application usage is significantly escalating, also for doing illegitimate purposes, particularly to organize and coordinate criminal acts. They are practically exploited by the criminality groups involved when they target and interact with the victims, or try to run away from criminal occurrences. By systematically conducting a review to understand and to investigate IM applications topic coming from a number of scientific sources (e.g. manuscripts, metadata, files, etc.), a broader picture and opportunity to regain the valuable evidence in the investigation is academically provided. © 2018 IEEE.

关键词： Smartphones

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Optimal Resource Allocation for NOMA Wireless Networks

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computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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