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An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

computers, Materials & Continua 2022年第1期70卷 1315-1334页

作者： Naglaa F.Soliman Naglaa S.Ali Mahmoud I.Aly Abeer D.Algarni Walid El-Shafai Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt International Academy for Engineering and Media Science October CityEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent *** diagnosis is essential to handle breast cancer patients for treatment at the right *** with mammography is the preferred examination for breast cancer,as it is available worldwide and ***-Aided Detection(CAD)systems are used to analyze medical images to detect breast cancer,*** death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after *** of mammogram images has four main steps:pre-processing,segmentation of the region of interest,feature extraction and classification of the images into normal or abnormal *** paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect *** pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter,removal of artifacts using morphological operations,and contrast enhancement using a fuzzy enhancement *** proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure(EME)and local contrast *** comparison shows an outstanding performance of the proposed technique from the visual and numerical *** segmentation process is performed using Otsu’smultiple thresholding *** method segments the image regions into five classes with variable intensities using four *** effectiveness is measured based on visual quality of the segmentation output,as it gives details about the image and positions of *** performance of the proposed framework is measured using Dice coefficient,Hausdorff,and Peak Signal-to-Noise Ratio(PSNR)*** segmented tumor region with the proposed segmentation method is 81%of the ground truth region provided by an ***,the proposed framew

关键词： Breast cancer mammogram images CAD contrast enhancement fuzzy logic segmentation

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Forecasting El Niño Six Months in Advance Utilizing Augmented Convolutional Neural Network

Forecasting El Niño Six Months in Advance Utilizing Augment...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Mohammad Naisipour Iraj Saeedpanah Arash Adib Mohammad Hossein Neisi Pour Department of Civil Engineering Faculty of Engineering University of Zanjan Iran Department of Computer Engineering Sharif University of Technology Tehran Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

One of the most significant phenomena related to large-scale climatic changes is the El Niño Southern Oscillation (ENSO), which can be instrumental in predicting dissasters across various regions of the globe. This study introduces a novel method aimed at predicting ENSO over a longer duration. This approach utilizes Convolutional Neural Networks (CNN) to anticipate ENSO occurrences up to six months ahead. The model utilizes anomaly maps as input data. Augmented Heat Content (HC) in conjunction with SST which is the surface temperature of the oceans are predictors, while the Niño 3.4 Index serves as the target for predictions. By applying convolutional tensors, the method extracts features from anomaly maps and processes them through a fully connected neural network to uncover relationships between the Niño Index and these features. A strategic data augmentation technique is employed to boost the quantity of merely HC input data, addressing the issue of limited observations related to recharged thermocline. The model has shown to provide consistent predictions when validated against observations over an extended period, confirming its reliability. Furthermore, the method's relatively low computational demand makes it a valuable tool for predicting ENSO and its related impacts.

关键词： Tensors computer network reliability Neural networks Feature extraction Data models Convolutional neural networks Indexes Reliability Forecasting Ocean temperature

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A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 533-546页

作者： Hao Wu Xin Luo MengChu Zhou Muhyaddin J.Rawa Khaled Sedraoui Aiiad Albeshri Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714and also with the University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA IEEE Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia College of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A large-scale dynamically weighted directed network(DWDN)involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications,like in a terminal interaction pattern analysis system(TIPAS).It can be represented by a high-dimensional and incomplete(HDI)tensor whose entries are mostly *** such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN.A latent factorization-of-tensors(LFT)model proves to be highly efficient in extracting such knowledge from an HDI tensor,which is commonly achieved via a stochastic gradient descent(SGD)***,an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale *** address this issue,this work proposes a proportional-integralderivative(PID)-incorporated LFT *** constructs an adjusted instance error based on the PID control principle,and then substitutes it into an SGD solver to improve the convergence *** studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models,the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

关键词： Big data high dimensional and incomplete(HDI)tensor latent factorization-of-tensors(LFT) machine learning missing data optimization proportional-integral-derivative(PID)controller

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Practical engineering Education: Use of collaborative robots in the context of Industry 5.0

Practical Engineering Education: Use of collaborative robots...

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2023 IEEE IFEES World engineering Education Forum and Global engineering Deans Council, WEEF-GEDC 2023

作者： Vogel, Christian Lindner, Fabian Kratzsch, Alexander Zittau/Görlitz University of Applied Science Faculty of Electrical Engineering and Computer Science Zittau Germany Zittau/Görlitz University of Applied Science Faculty of Business Administration and Engineering Zittau Germany Zittau/Görlitz University of Applied Science Institute of Process Technology Process Automation and Measuring Technology Zittau Germany

ISBN: (纸本)9798350316025

This scientific paper examines the use of collaborative robots in the context of Industry 5.0 and its impact on practical engineering education. The aim of this paper is to highlight the importance as well as the challenges of using robots in Industry 5.0 and to analyze how this can be integrated into engineering education. The paper discusses various aspects such as the technological advances, the need for interaction between humans and robots, and the competencies that engineers need in an increasingly digitalized working world. Furthermore, practical methods and approaches in engineering education at the Zittau/Görlitz University of Applied Sciences are presented to prepare students for the requirements of Industry 5.0. Additionally, a draft of a future interdisciplinary learning factory 5.0 will be presented, in which networked technologies with the focus on robotics run through various scenarios in the context of Industry 5.0 at different locations. © 2023 IEEE.

关键词： engineering education

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Relating university students’ online self-regulated English learning to motivational beliefs: a structural equation modelling analysis

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Behaviour and Information technology 2023年第7期42卷 888-903页

作者： Yossatorn, Yossiri Binali, Theerapong Weng, Cathy Awuor, Nicholas O. Department of General Education Faculty of Sciences and Health Navamindradhiraj University Bangkok Thailand Department of Computer Science and Information Engineering National Taiwan Normal University Taipei Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei Taiwan

This study investigated university students’ online self-regulated English learning (OSEL) and related it to their motivational beliefs in order to probe important psychological factors that may predict how students use self-regulated strategies to facilitate their online English learning. The data was gathered from 337 Thai university nursing students. The instruments used in this study were validated to measure motivational beliefs and OSEL in the context of Thai university students. All motivational belief factors were significantly associated with OSEL. The results of confirmatory factor analysis (CFA) indicated that the construct validity of the questionnaire items was acceptable. Furthermore, the structural modelling analysis (SEM) showed that intrinsic value and cost were significant predictors of all OSEL strategies, namely, behavioural strategies, goal setting, and environment structuring. Meanwhile, academic self-efficacy and attainment value were positively related to certain OSEL factors. Implications for enhancing students’ motivation and self-regulation in online language learning as well as directions for further investigations are discussed. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Students

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A review of federated learning in renewable energy applications:Potential,challenges,and future directions

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Energy and AI 2024年第3期17卷 444-457页

作者： Albin Grataloup Stefan Jonas Angela Meyer Bern University of Applied Sciences School of Engineering and Computer ScienceQuellgasse 21Biel2501Switzerland Universitàdella Svizzera italiana Faculty of InformaticsVia la Santa 1Lugano-Viganello6962Switzerland Delft University of Technology Department of Geoscience and Remote SensingStevinweg 1Delft2628The Netherlands

Federated learning has recently emerged as a privacy-preserving distributed machine learning *** learning enables collaborative training of multiple clients and entire fleets without sharing the involved training *** preserving data privacy,federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation,research and *** paper provides an overview of federated learning in renewable energy *** discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and *** also evaluate the potential and the challenges associated with federated learning applied in power and energy ***,we outline promising future research directions in federated learning for applications in renewable energy.

关键词： Federated learning Renewable energy Dis tributed learning Data heterogeneity Privacy Data sharing Wind energy Solar energy Buildings Review

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Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

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computers, Materials & Continua 2022年第4期71卷 457-471页

作者： Milos Mravik T.Vetriselvi K.Venkatachalam Marko Sarac Nebojsa Bacanin Sasa Adamovic Department of Computer Science Singidunum UniversityBelgrade11000Serbia Department of Computer science and Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

At present,the prevalence of diabetes is increasing because the human body cannot metabolize the glucose *** prediction of diabetes patients is an important research *** researchers have proposed techniques to predict this disease through data mining and machine learning *** prediction,feature selection is a key concept in ***,the features that are relevant to the disease are used for *** condition improves the prediction *** the right features in the whole feature set is a complicated process,and many researchers are concentrating on it to produce a predictive model with high *** this work,a wrapper-based feature selection method called recursive feature elimination is combined with ridge regression(L2)to form a hybrid L2 regulated feature selection algorithm for overcoming the overfitting problem of data *** is a major problem in feature selection,where the new data are unfit to the model because the training data are *** regression is mainly used to overcome the overfitting *** features are selected by using the proposed feature selection method,and random forest classifier is used to classify the data on the basis of the selected *** work uses the Pima Indians Diabetes data set,and the evaluated results are compared with the existing algorithms to prove the accuracy of the proposed *** accuracy of the proposed algorithm in predicting diabetes is 100%,and its area under the curve is 97%.The proposed algorithm outperforms existing algorithms.

关键词： Ridge regression recursive feature elimination random forest machine learning feature selection

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Impact of Parameters for the In-situ Measurement of the Electric Field Strength of the SSB Signal in a 5G NR System Using the Frequency-Selective Method in Zero-Span Mode

Impact of Parameters for the In-situ Measurement of the Elec...

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International Conference on Smart Systems and Technologies (SST)

作者： Vanja Mandrić Slavko Rupčić Bogdan Pavković Josip Ilakovac Department of Communication Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Measuring 5G systems is a major challenge for measurement engineers, who need to be aware of all the limitations of the new generation The parameters of the frequency-selective measurement system (spectrum analyzer - ROHDE & SCHWARZ FSH8) were investigated in the measurement of 5G NR SSB signals (3.4 – 3.8 GHz). The focus was on the setting parameters which, according to the literature, have an influence on the measurement: RBW, VBW, SWEEP TIME and the duration of the measurement. All measurements were carried out in zero-span mode. In the second part of the measurement, these SSB synchronization signal measurement results were compared with the 5G signal measurement results at the same location using the NARDA SRM-3006 device equipped with a decoder for the 5G NR system (FR1) to determine the optimal method for measuring these signals with a spectrum analyzer (SA).The measurement results show that the resolution bandwidth (RBW) has the largest variation in the measured electric field (EF) strength values of the SSB 5G NR signal of 17.20 dB (MH/RMS) and 17.70 (RMS/AGV10). It is therefore necessary to pay particular attention to the setting of this measurement parameter. In addition, the video resolution (VBW) shows a significantly lower field strength variation of 2.50 dB, but also the same deviation from the reference value of 2.50 dB (RMS/AGV10). Other analyzed parameters have a negligible influence on the measurement of the EF *** post-processing was performed in the work to determine the final (maximum) value of the EF strength at the points of assumed highest exposure, which is used to assess the impact and compliance with the permissible values according to the relevant standards.

关键词： Time-frequency analysis 5G mobile communication Measurement uncertainty Electric variables measurement Amplitude modulation Time measurement Frequency measurement Decoding Electric fields Signal resolution

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Sparse Attention-based Hierarchical Node Representation for Spatial Domain Identification

Sparse Attention-based Hierarchical Node Representation for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Peng, Wei Ping, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350386226

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM © 2024 IEEE.

关键词： Contrastive Learning

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Design and Implementation of Low-Cost Smart Monitoring System for Micro-Grid PV Applications Using LoRa technology

Design and Implementation of Low-Cost Smart Monitoring Syste...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Saadoon, Ghassan Mohammed Ghandour, Ahmad Mnati, Mohannad Jabbar Ali, Adnan Hussein Sabbar, Bayan Mahdi Kareem, Marwa Mahdi Islamic University of Lebanon Faculty of Engineering Department Communication Systems Department Beirut Lebanon Middle Technical University Institute of Technology Baghdad Department of Electrical Technology Baghdad Iraq Al-Mustaqbal University College of Engineering and Engineering Techniques Medical Instrumentation Engineering Techniques Department Babylon Iraq Middle Technical University Technical Management Institute Baghdad Computer Systems Techniques Department Baghdad Iraq

ISBN: (纸本)9798350309140

In this paper, an innovative smart monitoring system has been developed with a low cost for micro-grid photovoltaic systems using LoRa technology. This research addresses traditional monitoring solutions' limitations and explores the advantages of using LoRa technology in micro-grid PV systems. The literature review comprehensively highlights all aspects of micro-grid PV systems and underscores the significance of monitoring for performance enhancement and troubleshooting. The methodology section discusses the proposed system architecture, sensor selection, data collection, LoRa communication use, and data visualization. In the results sections, a real-life view of the effectiveness of a low-cost smart metering system is illustrated concerning issues such as the accuracy of data, transmission range, and energy consumption rate. Examples from real life are used to illustrate how the system improves performance, simplifies maintenance tasks, and helps identify problems in micro-grid PV systems. This research provides insights into exploiting LoRa technology to realize low-cost but efficient smart monitoring solutions in renewable energy microgrids. © 2024 IEEE.

关键词： Strain measurement

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