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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

Computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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Computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Towards Edge computing for 6G Internet of Everything: Challenges and Opportunities 1

Towards Edge Computing for 6G Internet of Everything: Challe...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Alawadhi, Abdulwadood Almogahed, Abdullah Azrag, Etidal Universiti Utara Malaysia School of Computing Kedah Malaysia Taiz University Faculty of Engineering and Information Technology Department of Software Engineering Taiz Yemen

ISBN: (纸本)9781665472753

The Internet of Everything (IoE)-based smart services are becoming more prominent as a result of the growing demands placed on wireless networks. Despite 5G having the ability to serve a wide range of IoE-based applications, they are unable to fully satisfy the needs of the newest intelligent systems. Therefore, 6G is the next generation for the IoT, IoE, and cellular networks, which aim to significantly improve smart services quality, such as maximum throughput and reduced latency. The number of internet-connected smart devices grows exponentially on a daily basis, resulting in Big Data. Several other cloud-based implementations use data centers as centralized servers for handling data collected by edge devices. This model places ever-increasing demands on computational infrastructure and communication, with unavoidable consequences for Experience and Quality-of-Service. By attempting to obtain cloud abilities closer to end users, edge computing is a novel technology which enables the development for 6G by attempting to overcome typical cloud weaknesses such as high latency and a lack of security. The purpose of this paper is to discuss challenges and opportunities in edge computing technology in 6G IoE. © 2023 IEEE.

关键词： Internet of Everything

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Industrial Image Anomaly Detection Method Based on Improved MAE 28

Industrial Image Anomaly Detection Method Based on Improved ...

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28th International Conference on Automation and computing, ICAC 2023

作者： He, Hui Xu, Yuanping Zhang, Chaolong Guo, Benjun Xu, Zhijie Jin, Jin Kong, Chao Huang, Jian School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Queensgate Huddersfield United Kingdom

ISBN: (纸本)9798350335859

This study explores an improved Masked Autoencoder (MAE) model for anomaly detection in common industrial scene images. Industrial image anomaly detection, as an important research topic in computer vision, can detect abnormal data deviations from normal expected behavior and ensure the normal operation of various systems. In actual industrial scenarios, the scarcity of abnormal samples, the cost of labeled data, and the lack of prior knowledge about anomalies make unsupervised learning methods widely used in the field of image anomaly detection. However, most unsupervised learning methods currently require the application of large-scale datasets. In order to make unsupervised learning applicable to small-scale industrial image datasets, we replaced the backbone network with Swin Transformer based on the Masked Autoencoder method, while improving its mask strategy and optimizer. The effectiveness and advantage of the proposed method are demonstrated through an experimental comparison with existing model, the results show that this method is better than MAE model pretrained on ImageNet, and outperforms other unsupervised learning models in multiple categories. © 2023 IEEE.

关键词： Anomaly detection

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AKGIN: An API Knowledge Graph and Intent Network based Mashup-Oriented API Recommendation Method 26

AKGIN: An API Knowledge Graph and Intent Network based Mashu...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yu, Changyong Hu, Rong Wang, Bangjun Hunan University of Science and Technology Computing and Novel Software Technology Hnust School of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350331684

With the continuous development of the Web API ecosystem, mashup-oriented API recommendation gets a lot of attention. Collaborative filtering, deep learning and their combination based methods are recently proposed for API recommendation. However, the recommendation results often perform poorly when data is sparse. Therefore, we propose an API knowledge graph (AKG) and intent network based mashup-oriented API recommendation method, whose main target is to recruit the semantic representation ability to improve the precision of API recommendation. Firstly, Latent Dirichlet Allocation (LDA) is used to extract API topics, which are important entities in the AKG. Then, the mashup intention is modeled based on the mashup-API interactions. The high-order relationship paths are recursively aggregated into the representation vector of Mashup and API. Finally, the inner product of their representations present the probability of the API recommendation for the mashup. The experimental results on real-world datasets show that our proposed method outperforms several baseline methods in mashup-oriented API recommendations. © 2023 IEEE.

关键词： Knowledge graph

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Performance Evaluation of Clustering Algorithms for Enhancing Test Case Prioritization in Regression Testing 4

Performance Evaluation of Clustering Algorithms for Enhancin...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Puviskar, S. Prabath Wasalthilaka, W.V.S.K. Kumara, B.T.G.S. Sabaragamuwa University of Sri Lanka Department of Computing & Information Systems Sri Lanka Sabaragamuwa University of Sri Lanka Department of Software Engineering Sri Lanka

ISBN: (纸本)9798350384864

Regression testing plays a crucial role in maintain software quality as applications evolving and getting more complex. Selection of a regression testing technique significantly influences overall software quality. In initial stages, this involves picking relevant test cases and remove unnecessary redundancies during minimization phase. Finally, prioritization of test cases phases most important ones is executed first which continue process iteratively or whole testing carried out which effect time and cost intensively. Test Case Prioritization method effective way to address the issue. In this study, we proposed approach to enhance Test Case Prioritization by integrating fault based methods and time of execution analysis. The study also evaluates the efficiency in proposed methodology through, employing APFD metric. The study aims to improve the precision and effectiveness of test case prioritization methods, advancing overall quality and reliability in dynamic software development environments. Notably, AHC outperforms in comparisons, showcasing its efficacy in improving outcomes. © 2024 IEEE.

关键词： software testing

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Intelligent Blocking and Prevention of SARS-CoV-2 Based on Evolutionary Reinforcement Learning 13

Intelligent Blocking and Prevention of SARS-CoV-2 Based on E...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： He, Chengwei Hong, Huaping Ma, Lijia Zhu, Qingling Bai, Yuan College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

In recent years, the global repercussions of SARS-CoV-2 and its variants have posed significant challenges to various areas, including the economic order, transportation, healthcare, and education, and the mitigation and prevention of SARS-CoV-2 and other infectious diseases have received much attention. This paper tries to develop a rational and effective intervention strategy in response to the dynamic evolution of the SARS-CoV-2 epidemic. To address this issue, we first use the SEIR model to analyze the population distribution under the SARS-CoV-2 epidemic, and then integrate a deep Q-neural network (DQN) to reflect an economic quantification model contextualized within the SARS-CoV-2 scenario. Next, we propose an evolutionary deep reinforcement learning to generate an optimal and adaptive intervention strategy, by optimizing the weight sequence of the DQN. The experimental results show the effectiveness of the proposed method in maximizing economic benefits, while ensuring the uninterrupted functionality of medical institutions. Importantly, the learned intervention strategy exhibits the applicability to other infectious diseases. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Multi-angle Text Recognition Algorithm 6

A Multi-angle Text Recognition Algorithm

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6th ACM International Conference on Multimedia in Asia, MMAsia 2024

作者： Wang, Jie Chen, Huilin Xue, Wandong Chen, Dongming Wang, Dongqi Software College of Northeastern University ShenYang China College of Engineering Computing and Cybernetics Australian National University Canberra Australia Northeastern University ShenYang China

ISBN: (纸本)9798400712739

Industrial digitalization serves as a pivotal factor in enhancing efficiency and minimizing costs. However, industrial design drawings, which are indispensable for conveying product information, pose significant challenges to computer vision systems due to their intricate nature. This necessitates the development of specialized text recognition algorithms tailored to these drawings. In this paper, we introduce a novel two-stage text recognition algorithm specifically designed for multi-angle industrial design drawings. The first stage employs a text direction correction network to accurately detect and rectify text orientation, thereby facilitating the recognition of skewed text by simplifying the process. The second stage leverages a lightweight network equipped with global and local mixing modules to effectively extract text. These modules capture both character dependencies and intricate details, thereby augmenting recognition accuracy without compromising processing speed. The efficacy of our proposed algorithm is rigorously demonstrated through extensive experimental evaluations conducted on comprehensive datasets of industrial design drawings. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Motivational factors and Challenges in the adoption of latest digital technology in educational institutes: A thematic analysis

Motivational factors and Challenges in the adoption of lates...

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2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Gulzar, Maryam Smolander, Kari Ali, Arshad Naqvi, Bilal Department of Software Engineering LUT University Lappeenranta53851 Finland FAST School of Computing National University of Computer and Emerging Sciences Lahore54770 Pakistan

Information technology infrastructure (ITI) has gone through an exponential change due to the rapid evolution of digital technologies during the past few years. It is therefore relevant to identify the factors that push organizations towards the adoption of new technologies. This paper considers a specific case study of the higher education institutions (HEI) to present (i) the motivational factors that push HEIs toward the adoption of new technologies, and (ii) the key challenges HEIs face in the adoption of these technologies. The data was gathered after conducting 17 open-ended interviews with practitioners working at HEIs in Finland and Pakistan. Data analysis identified 10 motivational factors and 41 challenges faced by HEI in the adoption of the latest technologies. Furthermore, the data was further classified to identify broad themes and categories representing the challenges and motivation factors. © 2024 The Author(s). Published by Elsevier B.V.

关键词： Challenges IT infrastructure Motivational factors Technology adoption

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SCFNet: A Spatial-Channel Features Network Based on Heterocentric Sample Loss for Visible-Infrared Person Re-identification 16th

SCFNet: A Spatial-Channel Features Network Based on Heteroc...

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16th Asian Conference on Computer Vision, ACCV 2022

作者： Su, Peng Liu, Rui Dong, Jing Yi, Pengfei Zhou, Dongsheng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

ISBN: (纸本)9783031262838

Cross-modality person re-identification between visible and infrared images has become a research hotspot in the image retrieval field due to its potential application scenarios. Existing research usually designs loss functions around samples or sample centers, mainly focusing on reducing cross-modality discrepancy and intra-modality variations. However, the sample-based loss function is susceptible to outliers, and the center-based loss function is not compact enough between features. To address the above issues, we propose a novel loss function called Heterocentric Sample Loss. It optimizes both the sample features and the center of the sample features in the batch. In addition, we also propose a network structure combining spatial and channel features and a random channel enhancement method, which improves feature discrimination and robustness to color changes. Finally, we conduct extensive experiments on the SYSU-MM01 and RegDB datasets to demonstrate the superiority of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Infrared imaging

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